Thursday, October 29, 2015

In a previous post, I pondered some questions related to using market demand functions to make welfare statements, following broadly Microeconomic Theory by Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green (MWG).

Making
welfare statements about aggregate demand revolves around a few key
concepts including a positive representative consumer, a wealth
distribution rule, and a
social welfare function. At a high level, these concepts seem to represent the technical assumptions and characteristics that need to hold in order to make most of the basic analysis of an intermediate microeconomics course mathematically sound or tractable for applied work. Here is a shot at some high level explanations:

This
rule or function is what allows us to write aggregate demand as a
function of prices and wealth in order to move forward with the rest of
our discussion about
welfare analysis.

Examples
given in MWG include wealth distribution rules that are a function of
shareholdings of stocks and commodities which make wealth a function of
the market's
price vector

social
welfare function (SWF) - this assigns utility to the vector of utilities for
all 'I' consumers in an economy or market. W(u1,.....,uI) or can be
written in terms
of indirect utilities W(v1,....vI).

Maximizing Social Welfare and Defining the Normative Representative Consumer

The
wealth distribution rule is assumed to maximize society's social
welfare function subject to a given level of aggregate wealth. The
optimal solution indicates
a particular indirect utility function v(p,w)

Normative
Representative Consumer- a positive representative consumer is a
normative representative consumer relative to social welfare function
W(.) if for every
(p,w) the distribution of wealth maximizes W(.). v(p,w) in the optimum
is the indirect utility function for the normal representative consumer.

For
v(p,w) to exist, we are assuming a SWF, and assuming it is maximized by an
optimal distribution of wealth according to some specified wealth
distribution rule.

An example from MWG: When v(p,w) is of the Gormon form, and the SWF is utilitarian, then an aggregate demand function can always be viewed as being generated by a normative representative consumer.

However, there are some theoretical issues in microeconomics that I either have forgotten, or never really understood that well.

Particularly these issues have to do with the strong axiom of revealed preference, the market aggregated demand function, and welfare analysis as discussed in one of my graduate texts (Microeconomic Theory by Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green). From that text (MWG) I basically get the following:

The strong axiom (SA) of revealed preference is equivalent to the symmetry and negative semi-definiteness of the slutsky substitution matrix

Violations of the SA mean cycling of choices or violations of transitivity

If observed demand follows the SA, then preferences that rationalize demand can always be recovered

It is impossible to find preferences that rationalize a demand function when the slutsky matrix is not symmetric

That means, for an individuals' observed demand function, if the slutsky matrix is not symmetric, you can't make welfare statements based on the area beneath the demand curve.

What happens when we aggregate individual demand to get a market demand function? It seems to me that the data of interest in most applied work is going to be related to an aggregate market demand curve. Based on Green et al:

the chances of the SA "being satisfied by a real economy are essentially zero"

If we allow individuals in an economy to have different preference relations/utility functions, when we aggregate to get a market demand function, the negative semi-definiteness of the slutsky matrix (equivalent to the weak axiom of revealed preference) might hold, but "symmetry will almost certainly not."

While positive effects of an equilibrium might hold, without symmetry the SA does not and we therefore cannot make statements about consumer welfare based on the area beneath an observed empirical market demand function

This last conclusion leaves me with a lot of questions to ponder:

What does that imply with regard to empirical work? It seems to not matter for positive effects (for instance a conclusion that a wage set above equilibrium causes a surplus of labor).

But, how much does it matter that I can't use an empirical demand function to calculate changes in consumer surplus for a change in prices? Maybe it only matters if I am interested in calculating some amount?

For any individual, if the SA might holds (which is possible), we certainly know a price increase would reduce consumer surplus, put them on a lower indifference curve and make them worse off. Regardless of the conclusions above, wouldn't that hold for all consumers represented by the aggregate market demand curve? Can't we make a normative statement (in terms of a qualitative directional sense even if we can't calculate total surplus) about all consumers even if the SA fails in the aggregate but holds for each individual?

Is this a case where one should just take the example of Milton Freidman's pool players who behave as if they
know physics? Maybe all of the assumptions (like the SA) fail to hold
for a market demand function, but we still feel confident making directional or qualitative welfare statements about price changes because everything else about the model predicts so well?

Any thoughts from readers?

I found it interesting, that the issues in the bulleted statements related to the MWG text were never addressed that I can tell in any of my undergraduate principles or intermediate micro texts, nor even in Nicholson's more advanced graduate text. It just seems like these texts jump from individual demand to market demand as a horizontal summation of individual's demand curves and go straight to welfare analysis and discussions about consumer surplus without discussing these issues related to the SA.

About Me

My primary interests are in applied econometrics with applications related to genomics, nutrition, health, and the environment. I have a quantitative and analytical background in the areas of applied economics and statistical genetics. I leverage my training with experience in machine learning and predictive modeling using SAS, R, and Python to solve problems. I can understand and produce peer reviewed research and discuss the application with a scientist, sales representative, or the customer whose problem ultimately drives the analysis. I can code my own estimators, execute SQL queries, parse text files, and visualize a social network.